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Li, J (2019). Robust Single Object Tracking Via Fully Convolutional Siamese Networks. Heidelberg University
Hering, F, Merle, M, Wierzimok, D and Jähne, B (1995). A robust technique for tracking particles over long image sequences. Proc. ISPRS Intercommission Workshop `From Pixels to Sequences', Zurich, March 22 - 24, 1995, In Int'l Arch. of Photog. and Rem. Sens. RISC Books. XXX-5W1 74--79
Jähne, B, Haußecker, H, Hering, F, Balschbach, G, Klinke, J, Lell, M, Schmund, D, Schultz, M, Schurr, U, Stitt, M and Platt, U (1996). The role of active vision in exploring growth, transport, and exchange processes. Aktives Sehen in technischen und biologischen Systemen, Workshop der GI-Fachgruppe 1.0.4. Bildverstehen Hamburg, 3--4. December 1996. infix. 4 194--202
Ommer, B (2013). The Role of Shape in Visual Recognition. Shape Perception in Human Computer Vision: An Interdisciplinary Perspective. Springer. 373--385PDF icon Technical Report (8.18 MB)
Kirchgeßner, N, Spies, H, Scharr, H and Schurr, U (2001). Root Growth Analysis in Physiological Coordinates. International Conference on Image Analysis and Processing (ICIAP'01)
Kirchgeßner, N, Spies, H, Scharr, H and Schurr, U (2001). Root Growth Measurements in Object Coordinates. Proceedings of the 23th DAGM Symposium on Pattern Recognition. Springer
Andres, B, Köthe, U, Kröger, T and Hamprecht, F A (2010). Runtime-Flexible Multi-dimensional Views and Arrays for C++98 and C++0x. ArXiv e-prints. http://arxiv.org/abs/1008.2909v1PDF icon Technical Report (415.54 KB)
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Haußmann, M, Hamprecht, F A and Kandemir, M (2019). Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation. UAI. ProceedingsPDF icon Technical Report (1.04 MB)
Friedrich, O, Weber, C, Both, M, von Wegner, F, Chamberlain, J S, Garbe, C S and Fink, R H A (2008). Sarcomere Structure and Motor-Protein Function in an Animal Model of Duchenne Muscular Dystrophy (mdx mouse). 87th Annual Meeting of the German Physiological Society
Haubold, C (2017). Scalable Inference for Multi-Target Tracking on Proliferating Cells. University of Heidelberg
Geese, M, Ruhnau, P and Jähne, B (2012). Scene based maximum likelihood PRNU and DSNU non uniformity correction. Forum Bildverarbeitung. KIT Scientific Publishing. 71--82. http://digbib.ubka.uni-karlsruhe.de/volltexte/1000030440
Mansfield, A, Gehler, P, Van Gool, L and Rother, C (2010). Scene carving: Scene consistent image retargeting. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6311 LNCS 143–156. www.fujifilm.com/products/3d/camera/finepix_
Richter, K E, Rocholz, R and Jähne, B (2012). The Schmidt Number Dependency of Air-Sea Gas Exchange with Varying Surfactant Coverage. SOLAS Open Science Conference, Washington State, USA
Görlitz, L, Singh, M and Schützbach, P (2007). Schnelle 3D-Vermessung von Partikeln in Rasterelektronenmiskroskopen mit Hilfe eines Rücksteuerdetektors
Berger, J, Neufeld, A, Becker, F, Lenzen, F and Schnörr, C (2015). Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations. Scale Space and Variational Methods in Computer Vision (SSVM 2015). Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-18461-6_32PDF icon Technical Report (364.01 KB)
Berger, J, Neufeld, A, Becker, F, Lenzen, F and Schnörr, C (2015). Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations. Scale Space and Variational Methods in Computer Vision (SSVM 2015)
Berger, J, Lenzen, F, Becker, F, Neufeld, A and Schnörr, C (2017). {Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations. J. Math. Imag. Vision. 58 102–129
Berger, J, Lenzen, F, Becker, F, Neufeld, A and Schnörr, C (2015). Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations. http://arxiv.org/abs/1507.06810
Berger, J, Lenzen, F, Becker, F, Neufeld, A and Schnörr, C (2015). Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations. http://arxiv.org/abs/1507.06810PDF icon Technical Report (4.42 MB)
Straehle, C N, Köthe, U, Briggman, K, Denk, W and Hamprecht, F A (2012). Seeded watershed cut uncertainty estimators for guided interactive segmentation. CVPR 2012. Proceedings. 765 - 772PDF icon Technical Report (2.84 MB)
Ommer, B, Mader, T and Buhmann, J M (2009). Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera. International Journal of Computer Vision. Springer. 83 57--71PDF icon Technical Report (9.61 MB)
Ommer, B (2008). Seeing The Objects Behind The Parts: Learning Compositional Models For Visual Recognition. VDM Verlag. http://www.amazon.com/Seeing-Objects-Behind-Parts-Compositional/dp/3639021444/ref=sr_1_1?ie=UTF8&s=books&qid=1232659136&sr=1-1
Kostrykin, L, Schnörr, C and Rohr, K (2018). Segmentation of Cell Nuclei Using Intensity-Based Model Fitting and Sequential Convex Programming. Proc. ISBI
Markowsky, P, Reith, S, Zuber, T E, König, R, Rohr, K and Schnörr, C (2017). Segmentation of cell structure using model-based set covering with iterative reweighting. Proc. ISBI
Andres, B, Köthe, U, Helmstaedter, M, Denk, W and Hamprecht, F A (2008). Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification. Pattern Recognition. 30th DAGM Symposium Munich, Germany, June 10-13, 2008. Proceedings. Springer. 5096 142-152PDF icon Technical Report (1.21 MB)
Schnörr, (1994). Segmentation of Visual Motion by Minimizing Convex Non-Quadratic Functionals. 12th Int. Conf. on Pattern Recognition. Jerusalem, Israel
Leue, C, Geißler, P, Jähne, B, Jähne, B, Geißler, P and Haußecker, H (1996). Segmentierung von Partikelbildern in der Strömungsvisualisierung. Proceedings of 18th DAGM-Symposium Mustererkennung. 118--129
Haubold, C, Schiegg, M, Kreshuk, A, Berg, S, Köthe, U and Hamprecht, F A (2016). Segmenting and Tracking Multiple Dividing Targets Using ilastik. Focus on Bio-Image Informatics. Springer. 219 199-229PDF icon Technical Report (4.46 MB)
Jähne, (1998). Sehen, was man sonst nicht sieht. Ruperto Carola. 32--36. http://www.uni-heidelberg.de/uni/presse/RuCa3_98/jaehne.htm
Andres, B, Hamprecht, F A and Garbe, C S (2007). Selection of Local Optical Flow Models by Means of Residual Analysis. Pattern Recognition. Springer. 4713 72-81PDF icon Technical Report (229.64 KB)
Andres, B, Garbe, C S, Schnörr, C and Jähne, B (2007). Selection of local optical flow models by means of residual analysis. Proceedings of the 29th DAGM Symposium on Pattern Recognition. Springer. 72--81
Staudacher, M, Hamprecht, F A and Görlitz, L (2009). Self Adjustment of Scanning Electron Microscopes / Selbstadaptivität von Rasterelektronenmikroskopen. Patent, Patent Number WO2009062781A1PDF icon Technical Report (46.64 KB)
Zisler, M, Zern, A, Petra, S and Schnörr, C (2019). Self-Assignment Flows for Unsupervised Data Labeling on Graphs. preprint: arXiv. https://arxiv.org/abs/1911.03472
Neigel, P (2017). Self-Similarity Based Detection Of Temporal Motifs In Multivariate Signals. Heidelberg University
Sümer, Ö, Dencker, T and Ommer, B (2017). Self-supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos. Proceedings of the IEEE International Conference on Computer Vision (ICCV)PDF icon Paper (3.98 MB)PDF icon Supplementary Material (3.36 MB)

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